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March 1, 1994IEEE Transactions on Medical Imaging261 citations

Tag and contour detection in tagged MR images of the left ventricle

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MGMoshe GuttmannTel Aviv UniversityJPJerry L. PrinceJohns Hopkins UniversityEMElliot R. McVeighCardiac Imaging

Key Result

A fully automated image processing algorithm successfully detected endocardial boundaries and tag lines in tagged MR images, though epicardial boundaries required more manual intervention.

Key Points

  • The aim is to develop a method for detecting myocardial contours and tags in left ventricular MR images.
  • Image processing steps to detect myocardial boundaries and tags in both short axis and long axis images.
  • Removal of tags using morphological closing and dynamic programming to find inner and outer boundaries.
  • Development of a graphical user interface for user correction of detection errors.
  • Automated detection successfully identifies the endocardial boundary and tag lines with minimal manual correction.
  • Epicardial boundary detection sometimes requires more user intervention for acceptable results.
  • Methods are being applied in the analysis of cardiac strain and different tag geometries.

Structured PICO

P
Population
Tagged MR images of a human left ventricle
I
Intervention
Hierarchy of image processing steps including morphological closing, dynamic programming, and least squares template matching, with a graphical user interface for correction
O
Outcome
Detection of endocardial and epicardial boundaries and tag linessurrogate

The proposed image processing hierarchy provides an effective, mostly automated method for detecting myocardial boundaries and tracking tags in cardiac MRI.

Limitations

  • Blood pooling, contiguous and adjacent tissue, and motion artifacts sometimes cause detection errors
  • Epicardial boundary sometimes requires more manual intervention to obtain an acceptable result

Abstract

Tracking magnetic resonance tags in myocardial tissue promises to be an effective tool for the assessment of myocardial motion. The authors describe a hierarchy of image processing steps which rapidly detects both the contours of the myocardial boundaries of the left ventricle and the tags within the myocardium. The method works on both short axis and long axis images containing radial and parallel tag patterns, respectively. Left ventricular boundaries are detected by first removing the tags using morphological closing and then selecting candidate edge points. The best inner and outer boundaries are found using a dynamic program that minimizes a nonlinear combination of several local cost functions. Tags are tracked by matching a template of their expected profile using a least squares estimate. Since blood pooling, contiguous and adjacent tissue, and motion artifacts sometimes cause detection errors, a graphical user interface was developed to allow user correction of anomalous points. The authors present results on several tagged images of a human. A fully automated run generally finds the endocardial boundary and the tag lines extremely well, requiring very little manual correction. The epicardial boundary sometimes requires more intervention to obtain an acceptable result. These methods are currently being used in the analysis of cardiac strain and as a basis for the analysis of alternate tag geometries.

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Cite This Study

Guttmann et al. (1994) studied Myocardial motion assessment. Automated contour and tag detection algorithm was evaluated on Detection of endocardial and epicardial boundaries and tag lines. A fully automated image processing algorithm successfully detected endocardial boundaries and tag lines in tagged MR images, though epicardial boundaries required more manual intervention.

synapsesocial.com/papers/6a087e0fab15ea61dee8e1e1https://doi.org/10.1109/42.276146
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